Machines such as, for example, off-highway haul trucks, motor graders, snow plows, and other types of heavy equipment are used to perform a variety of tasks. Some of these tasks involve carrying or pushing large, awkward, loose, and/or heavy loads up steep inclines or along rough or poorly marked haul roads. And, because of the size and momentum of the machines and/or because of poor visibility, these tasks can be difficult for a human operator to complete effectively.
To help guide the machines safely and efficiently along the haul roads, some machines are equipped with sensors, for example, RADAR sensors, SONAR sensors, LIDAR sensors, IR and non-IR cameras, and other similar sensors. These sensors are often connected to a visual display and/or a guidance system of the machine such that control over machine maneuvering may be enhanced or even automated. In order for these display and guidance systems to operate properly, the information provided by the sensors must be accurate. And, even though most machine sensor systems are calibrated when first commissioned, vibrations, collisions, and damage to the machine during operation can reduce the quality of information provided by the sensors. As such, periodic recalibration can be beneficial.
An exemplary method used to calibrate multiple machine-mounted sensors is described in U.S. Pat. No. 6,393,370 (the '370 patent) issued to Soika on May 21, 2002. Specifically, the '370 patent describes an autonomous mobile system having a plurality of sensors configured to survey objects within an immediate environment of the system. In order to improve accuracy of the autonomous mobile system, the system selectively implements a self test during which it turns around on its own axis within a static, but not necessarily known environment, and evaluates each sensor. During the self test, each sensor surveys individual cells of the environment to produce a cellularly-structured environmental map. Each sensor then classifies each cell of the map as either being occupied by an object or free of the object. The autonomous mobile system then evaluates an extent to which the classifications of individual sensors confirm one another. And, sensors whose measured results deviate from a great number of other sensors are classified as faulty. Faulty sensors can then either be indicated as being faulty, shut off, or calibrated based on the occupancy state classification.
Although the autonomous mobile system of the '370 patent may be helpful in detecting and calibrating faulty sensors, the benefit thereof may be limited. That is, the system may be beneficial only when calibrating sensors that detect occupancy states (i.e., sensors that detect a presence of an object) and, therefore, may have limited applicability to sensors that detect characteristics of an object. Further, the system of the '370 patent requires the mobile machine to interrupt its current task and turn about its axis in order to complete the self test. This interruption may reduce a productivity and efficiency of the machine. In addition, the system of the '370 patent may be limited to use with a single machine.
The disclosed sensor calibration system is directed to overcoming one or more of the problems set forth above and/or other problems of the prior art.